Prochains séminaires :

Abstract : A graph G is called normal if there exist two coverings, C and S, of its vertex set such that every member of C induces a clique in G, every member of S induces an independent set in G, and any clique in C and independent set in S have a non-empty intersection. Normal graphs derive their motivation from information theory, where they are related to the Shannon capacity of a graph. In particular, they form a family which extends the class of perfect graphs. It was conjectured by De Simone and Körner [DAM ’99] that a graph G is normal if G does not contain C_5, C_7 and the complement of C_7 as an induced subgraph. Using random graphs and rather routine probabilistic methods, we give a disproof of this conjecture.

Graphs allow to encode structural information included within
data used in chemical or pattern recognition problems. However,
conversely to vectors defined in an euclidean space, the
definition of a graph (dis)similarity measure is not
straightforward, but required to compute prediction models. One
of the most well known dissimilarity measure is the graph edit
distance. Despite its good interpretability, the computation of a
graph edit distance between two graphs is an NP-Hard
problem. Therefore, its application remains limited to small
graphs. During this presentation, I will introduce a formal
definition of this metric between graphs as a quadratic
assignment problem and some methods used in pattern recognition
to approximate an optimal solution. Considering approximations
allows us to apply this framework to chemoinformatics problems.

Séminaires précédents :

For a fixed collection of graphs F, the F-M-DELETION problem consists in, given a graph G and an integer k, decide whether there exists a set S of vertices of G of size at most k such that G without the vertices of S does not contain any of the graphs of F as a minor. This problem is a generalization of some well known problems as VERTEX COVER (F=K_2), FEEDBACK VERTEX SET (F=K_3), or VERTEX PLANARIZATION (F=K_5, K_3,3). We are interested in the parameterized complexity of F-M-DELETION when the parameter is the treewidth of the input graph, denoted by tw. Our objective is to determine, for a fixed F, the smallest function f such that F-M-DELETION can be solved in time f(tw)*poly(n) on n-vertex graphs.

We study the Steiner Tree problem, in which a set of terminal vertices
needs to be connected in the cheapest possible way in an edge-weighted
graph. This problem has been extensively studied from the viewpoint of
approximation and also parametrization. In particular, on one hand
Steiner Tree is known to be APX-hard, and W[2]-hard on the other, if
parameterized by the number of non-terminals (Steiner vertices) in the
optimum solution. In contrast to this we
give an efficient parameterized approximation scheme (EPAS), which
circumvents both hardness results. Moreover, our methods imply the
existence of a polynomial size approximate kernelization scheme
(PSAKS) for the assumed parameter.
We further study the parameterized approximability of other variants
of Steiner Tree, such as Directed Steiner Tree and Steiner Forest. For
neither of these an EPAS is likely to exist for the studied parameter :
for Steiner Forest an easy observation shows that the problem is
APX-hard, even if the input graph contains no Steiner vertices. For
Directed Steiner Tree we prove that computing a constant approximation
for this parameter is W[1]-hard. Nevertheless, we
show that an EPAS exists for Unweighted Directed Steiner Tree. Also we
prove that there is an EPAS and a PSAKS for Steiner Forest if in
addition to the number of Steiner vertices, the number of connected
components of an optimal solution is considered to be a parameter.

A chordless cycle is a cycle of length at least 4 that has no chord. We prove that the class of all chordless cycles has the Erdos-Posa property, which resolves the major open question concerning the Erdos-Posa property. We complement our main result by showing that the class of all chordless cycles of length at least l for any fixed l ≥ 5 does not have the Erdos-Posa property.

Our proof for chordless cycles is constructive : in polynomial time, one can either find k + 1 vertex-disjoint chordless cycles, or ck2 log k vertices hitting every chordless cycle for some constant c. It immediately implies an approximation algorithm of factor O(opt log opt) for Chordal Vertex Deletion, which improves the best known approximation by Agrawal et. al. The improved approximation algorithm entails improvement over the known kernelization for Chordal Vertex Deletion.

As a corollary, for a non-negative integral function w defined on the vertex set of a graph G, the minimum value \sum_x\in S w(x) over all vertex sets S where G − S is forest is at most O(k2 log k) where k is the maximum number of cycles (not necessarily vertex-disjoint) in G such that each vertex v is used at most w(v) times.

We survey some recent results on 2-edge and 2-vertex
connectivity in directed graphs. Despite being complete analogs of the
corresponding notions on undirected graphs, in digraphs 2-connectivity
has a much richer and more complicated structure. For undirected
graphs it has been known for over 40 years how to compute all bridges,
articulation points, 2-edge- and 2-vertex-connected components in
linear time, by simply using depth first search. In the case of
digraphs, however, the very same problems have been much more
challenging and have been tackled only very recently.

Jeudi 20 avril 2017 13:30-14:30 -
Aurélie Lagoutte - G-SCOP

Vendredi 28 avril 2017 14:15-15:30 -
Edouard Bonnet

Jeudi 11 mai 2017 14:00-15:00 -
Giorgio Lucarelli - Grenoble

Séminaire pole 2 de Giorgio Lucarelli

Lundi 12 juin 2017 14:00-15:00 -
Emily Speakman

Séminaire pole 2 Emily Speakman

Résumé : Title : Quantifying Double McCormick : Some Geometric Insight for Global Optimization Algorithms
When using the standard McCormick inequalities twice to convexify trilinear monomials, as is often the practice in modeling and software, there is a choice of which variables to group first. For the important case in which the domain is a nonnegative box, we calculate the volume of the resulting relaxation, as a function of the bounds defining the box. In this manner, we precisely quantify the strength of the different possible relaxations defined by all three groupings, in addition to the trilinear hull itself. As a by product, we characterize the best double McCormick relaxation.

Séminaire de Stéphane Canu - Variable selection and outlier detection as a MIP

Résumé : Title : Variable selection and outlier detection as a MIP
Abstract : Dimension reduction or feature selection is an effective strategy to handle contaminated data and to deal with high dimensionality while providing better prediction. To deal with outlier proneness and spurious variables, we propose a method performing the outright rejection of discordant observations together with the selection of relevant variables. To solve this problem, it is recasted as a mixed integer program which allows the use of efficient commercial solver. Also we propose an alternate projected gradient algorithm (proximal) so get a nice appoximated solution.